With the advent of an aging society, there is found a yearly increase in the number of patients with dementia-related diseases. There are several types of dementia-related diseases, thus necessitating an appropriate treatment depending on diseased conditions on the basis of differential diagnosis for these types.
On the other hand, in recent years, in order to cope with the above-described situation, radioactive medical examinations such as SPECT (Single Photon Emission Computed Tomography) and PET (Positron Emission Tomography) or other examinations such as CT (Computerized Tomography) and MRI can be used to obtain information on brain conditions (for example, refer to Japanese Published Unexamined Patent Application No. 2003-107161).
As a result, it has been found that phenomena such as a decrease in blood flow at a specific brain site and atrophy of tissues differ depending on the disease. Now demanded is a method for quantitatively evaluating these diseases.
For example, a local reduction in brain blood flow can be detected by comparison of images obtained by SPECT or PET.
Further, the atrophy of tissues can be detected abnormality by determining the volume of a specific site on the basis of MRI images and comparing the relative dimension thereof.
There is, for example, a method on the basis of the VBM (Voxel-Based Morphometry) method for processing MRI images, in which images of patients and those of healthy individuals are subjected to standardization by various types of image processing and compared statistically to extract a site of the local atrophy of brain tissues. When this method is used, physicians are able to make a diagnosis by referring to the distribution of atrophy sites and the extent of atrophy.
Since the processed result for diagnosis is critical information related to life, there is a need for an appropriate level of reliability. In particular, a highly accurate technique such as the VBM method targeting MRI images requires complicated processing of images. The technique also requires sufficient evaluation on whether specifications such as resolution dot density of input images, dynamic range of gray levels and image direction coincide with those expected by a system to be used or whether favorable processing results are obtained at individual steps of an entire processing flow.
Further, where the above-described brain images are used to determine the presence or absence of abnormalities, an ROI method is used in which a region of interest (ROI) having a predetermined dimension is established on an image (for example, refer to Statistical Analysis of SPECT, Image Diagnosis of Alzheimer's Dementia, Hiroshi Matsuda, Medical View Co., Ltd., pp 76 to 86 (2001)). According to this method, an ROI having a predetermined dimension is established at a specific site which focuses attention as a site involved in a specific disease to make a comparison on brain images.
However, in a conventional method, the specifications of the input image are confirmed and the processing results of the image are judged in most cases by a visual observation. Consequently, in the above judgment, the results may include subjective elements or there may be operational mistakes that overlook errors in processing. Further, there are instances where a large amount of patient data is desirably processed in a batch in order to construct a database and the like. In this case, there is a problem that many people are needed in evaluating the processing results.
Further, in the conventional ROI method in which an operator establishes an ROI by manually depicting the contour of a corresponding site on an image, the accuracy is likely to be influenced by accidental errors resulted from difference in visual perception or difference in operator's experience. Therefore, the conventional ROI method fails in providing diagnosis assistance on the basis of objective data, which is another problem.